OpenRaiser/Intern-Atlas huggingface dataset:
A Margin-based MLE for Crowdsourced Partial Ranking
Venue: arXiv cs.LG 2018
Year: 2018

A preference order or ranking aggregated from pairwise comparison data is commonly understood as a strict total order. However, in real-world scenarios, some items are intrinsically ambiguous in comparisons, which may very well be an inherent uncertainty of the data. In this case, the conventional total order ranking can not capture such uncertainty with mere global ranking or utility scores. In this paper, we are specifically interested in the recent surge in crowdsourcing applications to predict partial but more accurate (i.e., making less incorrect statements) orders rather than complete ones. To do so, we propose a novel framework to learn some probabilistic models of partial orders as a \emph{margin-based Maximum Likelihood Estimate} (MLE) method. We prove that the induced MLE is a joint convex optimization problem with respect to all the parameters, including the global ranking scores and margin parameter. Moreover, three kinds of generalized linear models are studied, including the basic uniform model, Bradley-Terry model, and Thurstone-Mosteller model, equipped with some theoretical analysis on FDR and Power control for the proposed methods. The validity of these models are supported by experiments with both simulated and real-world datasets, which shows that the proposed models exhibit improvements compared with traditional state-of-the-art algorithms.
The Second Amendment and Cyber Weapons - The Constitutional Relevance of Digital Gun Rights
Venue: arXiv cs.CY 2018
Year: 2018

In the future, the United States government can seek to limit the ownership and usage of cyber weapons. The question is whether the Second Amendment to the United States Constitution gives a right to bear and own military-grade cyber weapons, and if so, under which conditions. The framers of the Bill of Rights, ratified in 1791, did not limit the right to bear arms to defined weapons such as long rifles and pistols, but instead chose the broader word arms. The United States Supreme Court, in the case District of Columbia v. Heller, upheld a demarcation between dangerous and unusual weapons that are not permissible to own and weapons protected by the Second Amendment. Cyber weapons take the form of dual-use software, often shared and globally distributed that in most cases can be weaponized for harmful purposes. In recent years, major corporations have sought to hack back, and if the hack back is authorized, the question becomes whether corporations have digital gun rights. Even if corporations are considered US persons, they do not automatically obtain digital gun rights based on the Second Amendment. This article discusses the core constitutional challenges for the United States government in prohibiting individual ownership of cyber weapons and the rationale for why corporations are in a weaker position regarding ownership of cyber arms. The argument brought forward is that individuals can claim Second Amendment protection of their right to own military-grade software tools, but corporations must meet additional criteria to do so.
DataJoint: A Simpler Relational Data Model
Venue: arXiv cs.DB 2018
Year: 2018

The relational data model offers unrivaled rigor and precision in defining data structure and querying complex data. Yet the use of relational databases in scientific data pipelines is limited due to their perceived unwieldiness. We propose a simplified and conceptually refined relational data model named DataJoint. The model includes a language for schema definition, a language for data queries, and diagramming notation for visualizing entities and relationships among them. The model adheres to the principle of entity normalization, which requires that all data -- both stored and derived -- must be represented by well-formed entity sets. DataJoint's data query language is an algebra on entity sets with five operators that provide matching capabilities to those of other relational query languages with greater clarity due to entity normalization. Practical implementations of DataJoint have been adopted in neuroscience labs for fluent interaction with scientific data pipelines.
Is One Hyperparameter Optimizer Enough?
Venue: arXiv cs.AI 2018
Year: 2018

Hyperparameter tuning is the black art of automatically finding a good combination of control parameters for a data miner. While widely applied in empirical Software Engineering, there has not been much discussion on which hyperparameter tuner is best for software analytics. To address this gap in the literature, this paper applied a range of hyperparameter optimizers (grid search, random search, differential evolution, and Bayesian optimization) to defect prediction problem. Surprisingly, no hyperparameter optimizer was observed to be `best' and, for one of the two evaluation measures studied here (F-measure), hyperparameter optimization, in 50\% cases, was no better than using default configurations. We conclude that hyperparameter optimization is more nuanced than previously believed. While such optimization can certainly lead to large improvements in the performance of classifiers used in software analytics, it remains to be seen which specific optimizers should be applied to a new dataset.
To Ship or Not to (Function) Ship (Extended version)
Venue: arXiv cs.DB 2018
Year: 2018

Sampling is often used to reduce query latency for interactive big data analytics. The established parallel data processing paradigm relies on function shipping, where a coordinator dispatches queries to worker nodes and then collects the results. The commoditization of high-performance networking makes data shipping possible, where the coordinator directly reads data in the workers' memory using RDMA while workers process other queries. In this work, we explore when to use function shipping or data shipping for interactive query processing with sampling. Whether function shipping or data shipping should be preferred depends on the amount of data transferred, the current CPU utilization, the sampling method and the number of queries executed over the data set. The results show that data shipping is up to 6.5x faster when performing clustered sampling with heavily-utilized workers.
Online Learning with an Almost Perfect Expert
Venue: arXiv cs.LG 2018
Year: 2018

We study the multiclass online learning problem where a forecaster makes a sequence of predictions using the advice of $n$ experts. Our main contribution is to analyze the regime where the best expert makes at most $b$ mistakes and to show that when $b = o(\log_4{n})$, the expected number of mistakes made by the optimal forecaster is at most $\log_4{n} + o(\log_4{n})$. We also describe an adversary strategy showing that this bound is tight and that the worst case is attained for binary prediction.
Persistence Atlas for Critical Point Variability in Ensembles
Venue: arXiv cs.GR 2018
Year: 2018

This paper presents a new approach for the visualization and analysis of the spatial variability of features of interest represented by critical points in ensemble data. Our framework, called Persistence Atlas, enables the visualization of the dominant spatial patterns of critical points, along with statistics regarding their occurrence in the ensemble. The persistence atlas represents in the geometrical domain each dominant pattern in the form of a confidence map for the appearance of critical points. As a by-product, our method also provides 2-dimensional layouts of the entire ensemble, highlighting the main trends at a global level. Our approach is based on the new notion of Persistence Map, a measure of the geometrical density in critical points which leverages the robustness to noise of topological persistence to better emphasize salient features. We show how to leverage spectral embedding to represent the ensemble members as points in a low-dimensional Euclidean space, where distances between points measure the dissimilarities between critical point layouts and where statistical tasks, such as clustering, can be easily carried out. Further, we show how the notion of mandatory critical point can be leveraged to evaluate for each cluster confidence regions for the appearance of critical points. Most of the steps of this framework can be trivially parallelized and we show how to efficiently implement them. Extensive experiments demonstrate the relevance of our approach. The accuracy of the confidence regions provided by the persistence atlas is quantitatively evaluated and compared to a baseline strategy using an off-the-shelf clustering approach. We illustrate the importance of the persistence atlas in a variety of real-life datasets, where clear trends in feature layouts are identified and analyzed.
Deep Hybrid Real and Synthetic Training for Intrinsic Decomposition
Venue: arXiv cs.CV 2018
Year: 2018

Intrinsic image decomposition is the process of separating the reflectance and shading layers of an image, which is a challenging and underdetermined problem. In this paper, we propose to systematically address this problem using a deep convolutional neural network (CNN). Although deep learning (DL) has been recently used to handle this application, the current DL methods train the network only on synthetic images as obtaining ground truth reflectance and shading for real images is difficult. Therefore, these methods fail to produce reasonable results on real images and often perform worse than the non-DL techniques. We overcome this limitation by proposing a novel hybrid approach to train our network on both synthetic and real images. Specifically, in addition to directly supervising the network using synthetic images, we train the network by enforcing it to produce the same reflectance for a pair of images of the same real-world scene with different illuminations. Furthermore, we improve the results by incorporating a bilateral solver layer into our system during both training and test stages. Experimental results show that our approach produces better results than the state-of-the-art DL and non-DL methods on various synthetic and real datasets both visually and numerically.
Predicting Conversion of Mild Cognitive Impairments to Alzheimer's Disease and Exploring Impact of Neuroimaging
Venue: arXiv cs.LG 2018
Year: 2018

Nowadays, a lot of scientific efforts are concentrated on the diagnosis of Alzheimer's Disease (AD) applying deep learning methods to neuroimaging data. Even for 2017, there were published more than a hundred papers dedicated to AD diagnosis, whereas only a few works considered a problem of mild cognitive impairments (MCI) conversion to the AD. However, the conversion prediction is an important problem since approximately 15% of patients with MCI converges to the AD every year. In the current work, we are focusing on the conversion prediction using brain Magnetic Resonance Imaging and clinical data, such as demographics, cognitive assessments, genetic, and biochemical markers. First of all, we applied state-of-the-art deep learning algorithms on the neuroimaging data and compared these results with two machine learning algorithms that we fit using the clinical data. As a result, the models trained on the clinical data outperform the deep learning algorithms applied to the MR images. To explore the impact of neuroimaging further, we trained a deep feed-forward embedding using similarity learning with Histogram loss on all available MRIs and obtained 64-dimensional vector representation of neuroimaging data. The use of learned representation from the deep embedding allowed to increase the quality of prediction based on the neuroimaging. Finally, the current results on this dataset show that the neuroimaging does affect conversion prediction, however, cannot noticeably increase the quality of the prediction. The best results of predicting MCI-to-AD conversion are provided by XGBoost algorithm trained on the clinical and embedding data. The resulting accuracy is 0.76 +- 0.01 and the area under the ROC curve - 0.86 +- 0.01.
Mechanomyography based closed-loop Functional Electrical Stimulation cycling system
Venue: arXiv cs.RO 2018
Year: 2018

Functional Electrical Stimulation (FES) systems are successful in restoring motor function and supporting paralyzed users. Commercially available FES products are open loop, meaning that the system is unable to adapt to changing conditions with the user and their muscles which results in muscle fatigue and poor stimulation protocols. This is because it is difficult to close the loop between stimulation and monitoring of muscle contraction using adaptive stimulation. FES causes electrical artefacts which make it challenging to monitor muscle contractions with traditional methods such as electromyography (EMG). We look to overcome this limitation by combining FES with novel mechanomyographic (MMG) sensors to be able to monitor muscle activity during stimulation in real time. To provide a meaningful task we built an FES cycling rig with a software interface that enabled us to perform adaptive recording and stimulation, and then combine this with sensors to record forces applied to the pedals using force sensitive resistors (FSRs), crank angle position using a magnetic incremental encoder and inputs from the user using switches and a potentiometer. We illustrated this with a closed-loop stimulation algorithm that used the inputs from the sensors to control the output of a programmable RehaStim 1 FES stimulator (Hasomed) in real-time. This recumbent bicycle rig was used as a testing platform for FES cycling. The algorithm was designed to respond to a change in requested speed (RPM) from the user and change the stimulation power (% of maximum current mA) until this speed was achieved and then maintain it.
Active Learning for Interactive Neural Machine Translation of Data Streams
Venue: arXiv cs.CL 2018
Year: 2018

We study the application of active learning techniques to the translation of unbounded data streams via interactive neural machine translation. The main idea is to select, from an unbounded stream of source sentences, those worth to be supervised by a human agent. The user will interactively translate those samples. Once validated, these data is useful for adapting the neural machine translation model. We propose two novel methods for selecting the samples to be validated. We exploit the information from the attention mechanism of a neural machine translation system. Our experiments show that the inclusion of active learning techniques into this pipeline allows to reduce the effort required during the process, while increasing the quality of the translation system. Moreover, it enables to balance the human effort required for achieving a certain translation quality. Moreover, our neural system outperforms classical approaches by a large margin.
Coherent Explicit Dictionary Application for Haskell: Formalisation and Coherence Proof
Venue: arXiv cs.PL 2018
Year: 2018

Type classes are one of Haskell's most popular features and extend its type system with ad-hoc polymorphism. Since their conception, there were useful features that could not be offered because of the desire to offer two correctness properties: coherence and global uniqueness of instances. Coherence essentially guarantees that program semantics are independent from type-checker internals. Global uniqueness of instances is relied upon by libraries for enforcing, for example, that a single order relation is used for all manipulations of an ordered binary tree. The features that could not be offered include explicit dictionary application and local instances, which would be highly useful in practice. We propose a new design for offering explicit dictionary application, without compromising coherence and global uniqueness. We introduce a novel criterion based on GHC's type argument roles to decide when a dictionary application is safe with respect to global uniqueness of instances. We preserve coherence by detecting potential sources of incoherence, and prove it formally. Moreover, our solution makes it possible to use local dictionaries. In addition to developing our ideas formally, we have implemented a working prototype in GHC. This report contains the full formalisation and coherence proof.
Model predictive control of linear systems with preview information: feasibility, stability and inherent robustness
Venue: arXiv cs.SY 2018
Year: 2018

The use of available disturbance predictions within a nominal model predictive control formulation is studied. The main challenge that arises is the loss of recursive feasibility and stability guarantees when a persistent disturbance is present in the model and on the system. We show how standard stabilizing terminal conditions may be modified to account for the use of disturbances in the prediction model. Robust stability and feasibility are established under the assumption that the disturbance change across sampling instances is limited.
Harmonic-Percussive Source Separation with Deep Neural Networks and Phase Recovery
Venue: arXiv cs.SD 2018
Year: 2018

Harmonic/percussive source separation (HPSS) consists in separating the pitched instruments from the percussive parts in a music mixture. In this paper, we propose to apply the recently introduced Masker-Denoiser with twin networks (MaD TwinNet) system to this task. MaD TwinNet is a deep learning architecture that has reached state-of-the-art results in monaural singing voice separation. Herein, we propose to apply it to HPSS by using it to estimate the magnitude spectrogram of the percussive source. Then, we retrieve the complex-valued short-time Fourier transform of the sources by means of a phase recovery algorithm, which minimizes the reconstruction error and enforces the phase of the harmonic part to follow a sinusoidal phase model. Experiments conducted on realistic music mixtures show that this novel separation system outperforms the previous state-of-the art kernel additive model approach.
Planning for Muscular and Peripersonal-Space Comfort during Human-Robot Forceful Collaboration
Venue: arXiv cs.RO 2018
Year: 2018

This paper presents a planning algorithm designed to improve cooperative robot behavior concerning human comfort during forceful human-robot physical interaction. Particularly, we are interested in planning for object grasping and positioning ensuring not only stability against the exerted human force but also empowering the robot with capabilities to address and improve human experience and comfort. Herein, comfort is addressed as both the muscular activation level required to exert the cooperative task, and the human spatial perception during the interaction, namely, the peripersonal space. By maximizing both comfort criteria, the robotic system can plan for the task (ensuring grasp stability) and for the human (improving human comfort). We believe this to be a key element to achieve intuitive and fluid human-robot interaction in real applications. Real HRI drilling and cutting experiments illustrated the efficiency of the proposed planner in improving overall comfort and HRI experience without compromising grasp stability.
A Double Jaw Hand Designed for Multi-object Assembly
Venue: arXiv cs.RO 2018
Year: 2018

This paper presents a double jaw hand for industrial assembly. The hand comprises two orthogonal parallel grippers with different mechanisms. The inner gripper is made of a crank-slider mechanism which is compact and able to firmly hold objects like shafts. The outer gripper is made of a parallelogram that has large stroke to hold big objects like pulleys. The two grippers are connected by a prismatic joint along the hand's approaching vector. The hand is able to hold two objects and perform in-hand manipulation like pull-in (insertion) and push-out (ejection). This paper presents the detailed design and implementation of the hand, and demonstrates the advantages by performing experiments on two sets of peg-in-multi-hole assembly tasks as parts of the World Robot Challenge (WRC) 2018 using a bimanual robot.
Monolithic cut finite element based approaches for fluid-structure interaction
Venue: arXiv cs.CE 2018
Year: 2018

Cut finite element method (CutFEM) based approaches towards challenging fluid-structure interaction (FSI) are proposed. The different considered methods combine the advantages of competing novel Eulerian (fixed-grid) and established Arbitrary-Lagrangian-Eulerian (ALE) (moving mesh) finite element formulations for the fluid. The objective is to highlight the benefit of using cut finite element techniques for moving domain problems and to demonstrate their high potential with regards to simplified mesh generation, treatment of large structural motions in surrounding flows, capturing boundary layers, their ability to deal with topological changes in the fluid phase and their general straightforward extensibility to other coupled multiphysics problems. In addition to a pure fixed-grid FSI method, also advanced fluid domain decomposition techniques are considered rendering in highly flexible discretization methods for the FSI problem. All stabilized formulations include Nitsche-based weak coupling of the phases supported by the ghost penalty technique for the flow field. For the resulting systems, monolithic solution strategies are presented. Various 2D and 3D FSI-cases of different complexity validate the methods and demonstrate their capabilities and limitations in different situations.
Automating Requirements Traceability: Two Decades of Learning from KDD
Venue: arXiv cs.SE 2018
Year: 2018

This paper summarizes our experience with using Knowledge Discovery in Data (KDD) methodology for automated requirements tracing, and discusses our insights.
Markerless Visual Robot Programming by Demonstration
Venue: arXiv cs.CV 2018
Year: 2018

In this paper we present an approach for learning to imitate human behavior on a semantic level by markerless visual observation. We analyze a set of spatial constraints on human pose data extracted using convolutional pose machines and object informations extracted from 2D image sequences. A scene analysis, based on an ontology of objects and affordances, is combined with continuous human pose estimation and spatial object relations. Using a set of constraints we associate the observed human actions with a set of executable robot commands. We demonstrate our approach in a kitchen task, where the robot learns to prepare a meal.
Call Detail Records Driven Anomaly Detection and Traffic Prediction in Mobile Cellular Networks
Venue: arXiv cs.LG 2018
Year: 2018

Mobile networks possess information about the users as well as the network. Such information is useful for making the network end-to-end visible and intelligent. Big data analytics can efficiently analyze user and network information, unearth meaningful insights with the help of machine learning tools. Utilizing big data analytics and machine learning, this work contributes in three ways. First, we utilize the call detail records (CDR) data to detect anomalies in the network. For authentication and verification of anomalies, we use k-means clustering, an unsupervised machine learning algorithm. Through effective detection of anomalies, we can proceed to suitable design for resource distribution as well as fault detection and avoidance. Second, we prepare anomaly-free data by removing anomalous activities and train a neural network model. By passing anomaly and anomaly-free data through this model, we observe the effect of anomalous activities in training of the model and also observe mean square error of anomaly and anomaly free data. Lastly, we use an autoregressive integrated moving average (ARIMA) model to predict future traffic for a user. Through simple visualization, we show that anomaly free data better generalizes the learning models and performs better on prediction task.